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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications VII"

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3rd Workshop on Intelligent Data - From Data to Knowledge, DOING 2022, 1st Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2022, 4th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2022, 2nd Workshop on advanced Data systems Management, Engineering, and Analytics, MegaData 2022, 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage, SWODCH 2022 and Doctoral Consortium which accompanied 26th European conference on Advances in Databases and Information systems, ADBIS 2022

3rd Workshop on Intelligent Data - From Data to Knowledge, D...

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3rd Workshop on Intelligent Data - From Data to Knowledge, DOING 2022, 1st Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2022, 4th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2022, 2nd Workshop on advanced Data systems Management, Engineering, and Analytics, MegaData 2022, 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage, SWODCH 2022 and Doctoral Consortium which accompanied 26th European conference on Advances in Databases and Information systems, ADBIS 2022

ISBN: (纸本)9783031157424

The proceedings contain 61 papers. The special focus in this conference is on Intelligent Data - From Data to Knowledge. The topics include: Embedding Process Structure in Activities for Process Mapping and Comparison;Bulk Loading of the Secondary Index in LSM-Based Stores for Flash Memory;blink: Lightweight Sample Runs for Cost Optimization of Big Data applications;A RoBERTa Based Approach for Address Validation;empirical Comparison of Semantic Similarity Measures for Technical Question Answering;denoising Architecture for Unsupervised Anomaly Detection in Time-Series;discretizing Numerical Attributes: An Analysis of Human Perceptions;interactive Knowledge Graph Querying Through Examples and Facets;Understanding Misinformation About COVID-19 in WhatsApp Messages;It’s Too Noisy in Here: Using Projection to Improve Differential Privacy on RDF Graphs;modeling and Querying sensor Networks Using Temporal Graph Databases;indexing Continuous Paths in Temporal Graphs;unsupervised Extractive Text Summarization Using Frequency-Based Sentence Clustering;experimental Comparison of Theory-Guided Deep Learning Algorithms;hyper-parameters Tuning of Artificial Neural Networks: An Application in the Field of Recommender systems;exploring Latent Space Using a Non-linear Dimensionality Reduction Algorithm for Style Transfer Application;a Novel Method for University Ranking: Bounded Skyline;QLDT: A Decision Tree Based on Quantum Logic;using Provenance in Data Analytics for Seismology: Challenges and Directions;automatic Inference of Taxonomy Relationships Among Legal Documents;storing Feature Vectors in Relational Image Data Warehouses;automatic Classification of Stigmatizing Articles of Mental Illness: The Case of Portuguese Online Newspapers;a Multiplex Network Framework Based Recommendation systems for Technology Intelligence;relation Extraction from Clinical Cases for a Knowledge Graph;privacy Operators for Semantic Graph Databases as Graph Rewriting;a Collaborative Filtering Appr

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Decentralized Air Quality Management using Edge AI and Real-Time Control

Decentralized Air Quality Management using Edge AI and Real-...

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Augmented Intelligence and Sustainable systems (ICAISS), International conference on

作者： Balamanikandan A Shaik Rahamtula Sundar S Suresh Velayudham Chinnarasu K Chillara Jagadeesh Electronics and Communication Engineering Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati India Electronics and Communication Engineering Ramireddy Subbarami Reddy Engineering College Kadanuthala India Department of Mechanical Engineering Dr.M.G.R. Educational and Research Institute Deemed to be University Chennai India Department of Mechanical Engineering Sona College of Technology Salem India Department of Agriculture Engineering PPG Institute of Technology Coimbatore India

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

This research introduces a groundbreaking approach to real-time air quality monitoring and control by implementing Edge AI on an Arduino microcontroller. The system is designed to integrate an array of sensors that capture real-time air quality data, which is subsequently processed by optimized machine learning models deployed directly on the edge device. This innovative approach removes the need for cloud connectivity, significantly reducing latency, and enabling a swift response to fluctuating air quality conditions. The use of lightweight neural networks, such as Mobile Net or Efficient Net, ensures the system is highly efficient and suitable for resource-constrained devices. Real-time control algorithms, driven by AI assessments, dynamically adjust air purifiers, ventilation systems, and other mitigation measures to maintain optimal indoor air quality. This Edge AI-powered solution offers a cost-effective and energy-efficient method for enhancing air quality in real-time, with potential applications ranging from smart homes to larger public spaces. By leveraging these advanced technologies, the system not only improves accuracy and efficiency but also empowers users to take proactive measures for their health and well-being, thereby contributing to a healthier and more sustainable environment.

关键词： Microcontrollers Neural networks Edge AI Smart homes Machine learning Air quality Real-time systems Pollution measurement Environmental monitoring sensor arrays

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Development of In-Mold Integration of EDA sensors Via Additive Printing

Development of In-Mold Integration of EDA Sensors Via Additi...

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Intersociety conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Pradeep Lall Hyesoo Jang Scott Miller NSF-CAVE3 Electronics Research Center Auburn University Auburn AL NextFlex Manufacturing Institute San Jose CA

ISBN: (数字)9798350364330

ISBN: (纸本)9798350364347

Wire harnesses account for a significant weight of the vehicle. The emergence of advanced driver assistance systems has seen the emergence of a number of safety features, including lane departure warning, driver alertness monitoring, cross-traffic alert, guidance, navigation, and control. Touch surfaces in the automotive platform are presently used for little more than aesthetic appeal. However, the integration of human-machine interfaces on the touch surfaces requires additively printed in-mold electronics. The processes for the printing of sensors in addition to the integration of surface mount electronics for signal processing are not yet known for additively printed conformal electronics. This research focuses on developing robust and reliable PCBs for wearable electro-dermal activity (EDA) biosensors, particularly on substrates that are thermoformable. Electrodermal sensors measure the surface conductance of the skin to determine the state of the autonomous nervous system. The Electrodermal Activity (EDA) sensors are used for their ability to monitor a multitude of biosignals within the human body. These wearables could acquire biosignals with high accuracy, which makes them beneficial in scenarios like assessing a driver’s emotional and medical condition during various driving scenarios. The potential real-world implications of such monitoring are considerable, from safety applications to medical diagnostics. The purpose of using thermoformable substrates was due to their ability to be reshaped to provide unprecedented design flexibility, such as mounting on any curved surface. The other focus was to accurately measure skin conductance and response as biosignals for real-time driver monitoring. The approach will involve using the Voltera additive print platform for the fabrication of EDA sensors along with the signal processing circuits for the measurement of surface conductance. The influence of the sintering time temperature, print speed, print volume, and t

关键词： Temperature measurement Temperature sensors Measurement uncertainty sensor phenomena and characterization sensor systems Biomedical monitoring Wearable devices Surface treatment Monitoring Wearable sensors

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Data Security Analysis of PHY Layer Using Artificial Intelligence and Machine Learning

Data Security Analysis of PHY Layer Using Artificial Intelli...

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International conference on advanced Computing and Communication systems (ICACCS)

作者： Panduraju Pagidimalla T. Venketbabu J. K. Kanimozhi S. Renuka Nandula Anuradha S. Surenderkumar Department of Computer Science and Engineering KLH is Deemed to be a University Hyderabad Telangana India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Namakkal (DT) Department of IT Vasavi College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana Department ECE Sri Eshwar College of Engineering Coimbatore TamilNadu India

Artificial Intelligence (AI) and Machine Learning (ML) plays a critical role in determining the future of wireless networks. The advancements in technology will unleash a significant Internet of Things (IoT) where numerous interconnected processes, people and devices will be served in a simultaneous manner. Open and broadcast wireless channels make wireless networks particularly susceptible to espionage, jamming, and disturbance attacks. Physical Layer (PHY layer) security becomes a major concern since typical secure methods do not operate well in wireless PHY networks. As a starting point, we examine the PHY layer of wireless connections for threats and hazards. When it comes to PHY layer security systems, there are three basic categories: Spatial Domain-Based (SDB), Temporal (TTB), and Frequency (FF). We will weigh the benefits and drawbacks of the most recent safe technology in each area along the way. The methodologies and methods employed in these areas, as well as the open research questions and prospects in this field, are also discussed.

关键词： Wireless sensor networks Power demand Wireless networks Data security Machine learning Physical layer Communication system security

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advanced Ultra Low-Power Deep Learning applications with Neuromorphic Computing

Advanced Ultra Low-Power Deep Learning Applications with Neu...

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IEEE conference on High Performance Extreme Computing (HPEC)

作者： Mark Barnell Courtney Raymond Lisa Loomis Darrek Isereau Daniel Brown Francesca Vidal Steven Smiley Air Force Research Laboratory Information Directorate Rome NY USA Defense Systems & Solutions SRC Inc. North Syracuse NY USA

The latest Intel neuromorphic processor, Loihi 2, provides a breakthrough in Artificial Intelligence (AI) for computing at the edge, where sensor information is collected. The computing architecture does this by leveraging computations at the transistor level in a fashion analogous to the human brain's biological neural networks (vs. a Von Neumann compute architecture). The Loihi 2's high performance, small form factor, and low-power consumption makes it a unique capability that is well suited for use in devices. Our technical approach and findings support extreme computing needs for the internet of things (IoT) and various airborne platforms' applications. The recently released Loihi 2 and the novel research completed on this effort were combined to accelerate development and demonstration of a new concept of operation for machine learning at the edge. This research included the development of spiking neural networks (SNN) on sensor data representative of information sources from a small research platform. Our concept uses the representative sensor data to predict the platform mode through machine learning. Importantly, our technical approach allowed us to rapidly scale from IBM's TrueNorth Corelet framework to the Lava framework, which Intel's Loihi 2 neuromorphic processor utilizes. The use of the Lava framework demonstrates the art-of-the-possible in edge computing by demonstrating capabilities on small airborne platform sensor data and wide extensibility to other domains that can use this neuromorphic compute hardware. In summary, this research included the use of new compute frameworks, novel processing algorithms, and a unique concept of operation. This technical approach resulted in the classification of the platform mode given the sensor information with accuracies up to 97.6%.

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Design and Development of a Non-Invasive Glucose Monitoring Device Using advanced Microcontroller

Design and Development of a Non-Invasive Glucose Monitoring ...

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Expert Clouds and applications (ICOECA), International conference on

作者： S. Divya P Karthikeyan M. Vaigundamoorthi D Ramya Manzoore Elahi M. Soudagar Rajendiran M Department of Biomedical Engineering Dr. N.G.P. Institute of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Millions of individuals all over the world are living with diabetes, a chronic condition that necessitates regular monitoring of Blood Glucose Levels (BGL). The present technique for minimal invasive BGL monitoring involves taking a small blood sample and transferring it to a glucometer. However, this procedure can be uncomfortable for the patient. This research presents a method for non-invasively monitoring BGL with the help of a powerful controller called the ARM Cortex-M4. For non-invasive applications, we use photoplethysmography (PPG) signals. Using a photodiode and an infrared LED light, PPG samples are received from a finger sensor. Signal processing and feature extraction are carried out using the Gray-Level Co-occurrence Matrix (GLCM) method. To predict the BGL, two ML models are developed: one employing a Linear Regression (LR) model and the other an Artificial Neural Network (ANN) model. The experimental findings show that the ANN model is quite effective, with an R2 score of 0.986. After careful consideration, the ARM Cortex-M4 microcontroller's processing capabilities were found to be optimal for running the ANN model. The model must be fine-tuned before deployment so it can communicate with the microcontroller and provide correct inference in real time. The finger sensor simplifies PPG sample collection, and the OLED display makes it simple to view BGL.

关键词： Strips Microcontrollers Artificial neural networks Predictive models Organic light emitting diodes Signal processing Glucose

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From Vision to Precision: The Dynamic Transformation of Object Detection in Autonomous systems

From Vision to Precision: The Dynamic Transformation of Obje...

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Computing and Informatics (ICCI), International conference on

作者： Nesma Alabyad Ziad Hany Abdelrahman Mostafa Reem Eldaby Ibrahim Ayman Tagen Amal Mehanna Future University In Egypt Cairo Egypt

This paper explores the evolution of self-driving cars, from early radio-controlled prototypes to today's advanced autonomous vehicles, highlighting key milestones such as Houdina Radio Control's 1920s efforts and Carnegie Mellon University's Navlab project in the 1980s. Recent advancements driven by industry leaders like Google, Tesla, and Uber incorporate advanced technologies, including sensors, cameras, radar, and lidar, empowering self-driving cars for autonomous navigation. The literature review in Frontiers in Future Transportation focuses on performance metrics for automated driving systems, discussing methodologies for quantifying obstacle threats and enhancing human-like perception and response mechanisms, with a significant emphasis on deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Object detection algorithms' dynamic evolution, influenced by CNNs, is explored, and the integration of hardware accelerators like GPUs, FPGAs, and specialized AI accelerators is highlighted for real-time responsiveness. Deep learning applications in self-driving cars, including obstacle detection, lane recognition, and traffic sign and light recognition, are outlined. Vision sensor-based obstacle detection using monocular and binocular approaches is discussed, emphasizing the importance of binocular vision technology. Lane recognition's significance for deviation warning, collision prevention, and path planning is highlighted, along with deep learning approaches such as John et al.'s lane detection algorithm. Traffic sign and light recognition are explored for safety and regulatory compliance, utilizing CNNs and Deep Neural Networks (DNNs). The discussion on sensors covers simultaneous localization and mapping (SLAM) using both passive (digital cameras) and active sensors (LIDAR, RADAR), emphasizing the integration of sensors and pre-existing maps to enhance the SLAM process and contribute to the overall perception of a

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Wearable sensor-Based Gait Analysis: advanced Feature Extraction Techniques

Wearable Sensor-Based Gait Analysis: Advanced Feature Extrac...

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International conference on Advances in Information Technology (ICAIT)

作者： Karthik K Preethi Salian Ranjith H D Supriya Salian Jayashree K Annapoorneshwari Shetty Dept. of Electronics and Communication Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Dept. of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Dept. of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidri India Dept. of Computer Science and Engineering St. Joseph Engineering College Mangaluru India Dept. of Computer Science and Engineering NM Institute of Technology Bangalore India Dept. of Master of Computer Applications AIMIT St. Aloysius (Deemed to be University) Mangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Gait irregularities among adults are common in clinical settings and can result in severe consequences such as falls and injuries, leading to increased healthcare costs. Presently, gait evaluation primarily relies on subjective assessments using standardized clinical tools. Although motion capture systems like Qualisys and Vicon offer more objective analysis, they depend on costly, intricate equipment in specialized gait laboratories. Due to limited accessibility, involving factors like equipment scarcity, demand for technical expertise, in-person patient visits, complex procedures, and high costs, these methods remain largely inaccessible. Consequently, there is a pressing need for a more objective and cost-effective gait assessment solution. The proposed work introduces an economical automated feature extraction approach to address this issue. Leveraging Inertial Measurement Unit (IMU) sensors, comprising a three-axis gyroscope and accelerometer system captures precise foot position and orientation data crucial for comprehensive gait analysis. IMU sensors have gained popularity due to their adaptability in clinical environments and non-clinical settings like patient residences or community spaces. With the help of a microcontroller mounted on the barefoot, the IMU sensor collects raw gyroscope and accelerometer data for gait analysis. These data are wirelessly transmitted via Bluetooth to a computer for subsequent in-depth analysis, enabling effective feature extraction for comprehensive gait assessment.

关键词： Legged locomotion Accelerometers Costs Microcontrollers Pressing Feature extraction sensor systems Motion capture sensors Gyroscopes

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IoT-Enabled Automotive Software Development through Integration of Engine Control Unit Signals for Adaptive Autonomous Emergency Braking

IoT-Enabled Automotive Software Development through Integrat...

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Innovation in Technology (ASIANCON), Asian conference on

作者： Chinmayee Khondekar Priti Gade Department Of Electronics Engineering G. H. Raisoni College Of Engineering Nagpur (An Autonomous Educational Institute Affiliated to the Rashtrasant Tukdoji Maharaj Nagpur University)

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

This paper explores the integration of engine control unit (ECU) signals within an IoT-enabled automotive software development framework. The study focuses on leveraging ECU data for real-time decision-making in adaptive systems, enhancing automotive safety and performance through advanced software development and testing methodologies. It presents the design and implementation of an adaptive autonomous emergency braking (AEB) system for automotive safety applications, focusing on the integration of engine control unit (ECU) signals within an IoT-driven software framework. The system employs a network of sensors and cameras to continuously monitor the vehicle’s surroundings and detect potential obstacles. When the vehicle’s battery and ignition are switched on, the sensors/cameras send detected obstacle signals to the ECU, which in turn initiates signal processing via Controller Area Network (CAN) communication. The signal processing algorithm analyzes the obstacle’s distance and the vehicle’s speed to assess collision risks in real-time. Upon detecting a potential collision, the system triggers a warning on the Human-Machine Interface (HMI) to alert the driver. If the driver fails to respond promptly, the AEB system automatically activates the brakes to prevent a collision.

关键词： Technological innovation Vehicle safety Signal processing algorithms sensor systems Real-time systems sensors Engines Software development management Vehicles Testing

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Visual Perception Challenges in Adverse Weather for Autonomous Vehicles: A Review of Rain and Fog Impacts

Visual Perception Challenges in Adverse Weather for Autonomo...

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IEEE Information Technology, Networking, Electronic and Automation Control conference

作者： Yongsheng Qiu Yuanyao Lu Yuantao Wang Chaochao Yang School of Electrical and Control Engineering North China University of Technology Beijing China School of Information Science and Technology North China University of Technology Beijing China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Autonomous driving systems (ADS) offer efficient solutions for transportation, but their visual perception capabilities are significantly affected under adverse weather conditions such as rain and fog, posing a threat to vehicle safety. Rain droplets and fog can interfere with sensors, leading to inaccurate information acquisition and increased driving risks. Therefore, enhancing the environmental perception of ADS in adverse weather conditions is a critical research focus. This paper systematically evaluates the impacts and challenges of adverse weather conditions, specifically rain and fog, on ADS environmental perception sensors. It explores the imaging characteristics of rain and fog and their effects on visual perception, summarizing dehazing and deraining algorithms. Ad-ditionally’ this paper reviews the development status of advanced visual tasks such as object detection in autonomous driving under adverse weather conditions. The aim of this paper is to provide a comprehensive understanding of the reliability of ADS in adverse weather conditions, laying a foundation for future de-velopments and practical applications in autonomous driving technology.

关键词： Image sensors Visualization Rain Reviews Object detection sensor systems Autonomous vehicles Image enhancement Meteorology Visual perception

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